M.E., from O.Fr. simple, from L. simplus "simple, single," variant of simplex, from PIE root *sem- "one, together;" cf. Pers. ham "together," → com-, Skt. sam "together;" + *plac- "-fold," from PIE *plek- "to plait," → multiply.
Sâdé "simple, unmixed, smooth, erased, plain;" cf. Khotanese sāta- "smooth;" Baluchi sāt/sāy-, sāh- "to shave;" Av. si-, sā- "to sharpen, cut;" Skt. śā- "to sharpen, whet" (Cheung 2007); see also → precise.
Fr.: événement simple
Statistics: An event consisting of a single point of the → sample space.
Fr.: fraction simple
simple harmonic motion
jonbeš-e hamâhang-e sâdé
Fr.: mouvement harmonique
The motion of a body subjected to a restraining force which is directly proportional to the displacement from a fixed point in the line of motion. The equation of simple harmonic motion is given by x = A sin(ωt + θ0), where x is the body's displacement from equilibrium position, A is the → amplitude, or the magnitude of harmonic oscillations, ω is the → angular frequency, t is the time elapsed, and θ0 is the → initial phase angle.
simple harmonic oscillator
navešgar-e hamâhang-e sâdé
Fr.: oscillateur harmonique simple
Fr.: population simple
A set of stars resulting from a spatially (≤ few pc) and temporally (≤ Myr) correlated star formation event.
Fr.: racine simple
A generalization of the simplest closed configuration that can be made from straight line segments. For example, a → triangle is a 2-simplex because it is in two → dimensions, and → tetrahedron is a 3-simplex because it is in three dimensions (Steven Schwartzman, An Etymological Dictionary of Mathematical Terms Used in English, 1994).
Simplex, literally "uncomplicated, → simple," from sim-, from PIE root *sem- "one, once, together" + plek- "to fold." "folded [only] once."
Fr.: méthode du simplexe
An → algorithm for solving the classical → linear programming problem; developed by George B. Dantzig in 1947. The simplex method is an → iterative method, solving a system of → linear equations in each of its steps, and stopping when either the → optimum is reached, or the solution proves infeasible. The basic method remained pretty much the same over the years, though there were many refinements targeted at improving performance (e.g. using sparse matrix techniques), numerical accuracy and stability, as well as solving special classes of problems, such as mixed-integer programming (Free On-Line Dictionary of Computing, FOLDOC).